构建了1亿+规模的高质量图文数据集，助力文本生成图像研究开源可复现。
MONET: A Massive, Open, Non-redundant and Enriched Text-to-image dataset
- 从29亿原始配对中筛选并去重，经多轮过滤与多模型重描述
- 包含约1.049亿张图像-文本对，支持短长描述与预计算嵌入
- 适合作为大模型训练基础数据集，尤其适合追求可复现性的研究者
训练大型文本到图像模型需要高质量、经过精心筛选的数据集，涵盖多样化内容和详细描述。然而，在大规模下收集、过滤、去重和重新标注语料库的成本与复杂性，阻碍了该领域的开放与可复现研究。我们提出 MONET，一个开放的 Apache 2.0 许可数据集，包含约 1.049 亿个图像-文本对，源自 29 亿条原始配对，通过多个阶段的安全过滤、基于领域过滤、精确与近似重复去除，以及使用多种视觉-语言模型进行短至长形式的重描述，并进一步通过合成样本增强。每张图像均附带预计算嵌入与标注，以加速下游应用。为验证 MONET 的有效性，我们在其上仅训练了一个 40 亿参数的潜在扩散模型，达到与现有方法相当的 GenEval 与 DPG 得分，证明该数据集降低了大规模、可复现文本到图像研究的门槛。
原文摘要 · Abstract (English)
Training large text-to-image models requires high-quality, curated datasets with diverse content and detailed captions. Yet the cost and complexity of collecting, filtering, deduplicating, and re-captioning such corpora at scale hinders open and reproducible research in the field. We introduce MONET, an open Apache 2.0 dataset of approx. 104.9M image--text pairs collected from 2.9B raw pairs across heterogeneous open sources through successive stages of safety filtering, domain-based filtering, exact and near-duplicate removal, and re-captioning with multiple vision-language models covering short to long-form descriptions, and further augmented with synthetically generated samples. Each image is shipped with pre-computed embeddings and annotations to accelerate downstream use. To validate the effectiveness of MONET, we train a 4B-parameter latent diffusion model exclusively on it and reach competitive GenEval and DPG scores, demonstrating that our dataset lowers the barrier to large-scale, reproducible text-to-image research.
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